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Machine Learning Compiler Engineer Jobs in Virginia

Machine Learning Engineer Richmond, Virginia (5 Days Onsite) need local within commute About the Role We are seeking a Machine Learning Engineer with expertise in agentic AI systems to design, build ...

Machine Learning Engineer

Arlington, VA ยท Hybrid

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Machine Learning Engineer

Arlington, VA ยท Hybrid

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Machine Learning Engineer

Arlington, VA ยท On-site

$110K - $160K/yr

... machine learning, artificial intelligence, and computer vision * Perform rapid prototyping and enhanced development to be integrated into operational systems * Contribute your strong programming ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

Senior Machine Learning Engineer

Mclean, VA ยท On-site

$105K - $145K/yr

We are looking for a Senior Machine Learning Engineer to that will focus on researching, designing, training, and evaluating machine learning models to solve complex, real-world problems. We ...

Senior Machine Learning Engineer

Mclean, VA

$105K - $145K/yr

We are looking for a Senior Machine Learning Engineer to that will focus on researching, designing, training, and evaluating machine learning models to solve complex, real-world problems. We ...

Senior Machine Learning Engineer

Mclean, VA ยท On-site

$105K - $145K/yr

Your Mission, Should You Choose to Accept As a Machine Learning Engineer, you will research, evaluate, and select appropriate machine Learning approaches and architectures based on the problem ...

Sr. Machine Learning Engineer

Fort Belvoir, VA ยท On-site

$118K - $162K/yr

Role: Sr. Machine Learning Engineer Location: Ft. Belvoir, VA (On-site with Hybrid Option) Duration: Long Term Contract Clearance: DOD Top Secret Clearance (Must) As a consultant, will be working to ...

Lead Machine Learning Engineer

Mclean, VA ยท On-site +1

$103K - $136K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You ...

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Machine Learning Compiler Engineer information

See Virginia salary details

$31.2K

$127.7K

$191.8K

How much do machine learning compiler engineer jobs pay per year?

As of Jul 21, 2026, the average yearly pay for machine learning compiler engineer in Virginia is $127,665.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,600.00 and $153,700.00 per year, depending on experience, location, and employer.

What is a Machine Learning Compiler Engineer job?

A Machine Learning Compiler Engineer focuses on optimizing and building compilers that translate high-level machine learning models into efficient code runnable on specialized hardware (e.g., GPUs, TPUs). They work on improving performance, memory usage, and execution efficiency of ML workloads by designing compiler optimizations, code generation techniques, and leveraging frameworks like LLVM or MLIR. Their role bridges the gap between ML researchers and hardware engineers, ensuring models run efficiently on target platforms.

What are the typical daily responsibilities of a Machine Learning Compiler Engineer?

As a Machine Learning Compiler Engineer, your daily responsibilities often include designing and implementing new compiler optimizations, collaborating with machine learning researchers to support model deployment, and debugging performance or correctness issues in compiled code. You may participate in code reviews, write technical documentation, and conduct benchmarking to evaluate how machine learning models perform on various hardware backends. Close collaboration with hardware engineers, software architects, and data scientists is common, ensuring end-to-end solutions meet both research and production requirements. Staying updated with the latest advancements in both compiler technology and machine learning frameworks is also a key aspect of the role.

What are the key skills and qualifications needed to thrive in the Machine Learning Compiler Engineer position, and why are they important?

A Machine Learning Compiler Engineer needs a deep understanding of computer science fundamentals, compiler theory, and experience with machine learning frameworks, often supported by a relevant degree in computer science or engineering. Proficiency with tools such as LLVM, TVM, MLIR, and languages like C++, Python, and CUDA is typically required, and familiarity with hardware architectures is a plus. Strong problem-solving, teamwork, and communication skills are essential for collaborating with cross-functional teams and addressing complex system issues. These capabilities are important for designing and optimizing compilers that enable scalable and efficient deployment of machine learning models on diverse hardware platforms.

What are the most commonly searched types of Machine Learning Compiler Engineer jobs in Virginia? The most popular types of Machine Learning Compiler Engineer jobs in Virginia are:

Machine Learning Engineer

WorkNovas LLC

Richmond, VA โ€ข On-site

Contractor

Posted 28 days ago


Job description

Machine Learning Engineer ย 

Richmond, Virginia (5 Days Onsite) need local within commute

About the Role
We are seeking a Machine Learning Engineer with expertise in agentic AI systems to design, build, and deploy next-generation AI solutions. In this role, you will work at the intersection of LLMs, autonomous agents, retrieval-augmented generation (RAG), and enterprise-scale systems, leveraging Azure AI Foundry, Copilot Studio, and modern orchestration frameworks.
You will collaborate closely with product managers, architects, and application teams to deliver intelligent, production-grade AI agents that integrate seamlessly with business workflows and enterprise data.
Key Responsibilities
Design and implement agentic AI systems capable of planning, tool use, memory, and multi-step reasoning
Build and deploy AI solutions using Azure AI Foundry and Copilot Studio
Develop RAG pipelines integrating structured and unstructured enterprise data
Implement and optimize vector databases for semantic search and long-term agent memory
Orchestrate LLM-based agents using frameworks such as LangChain (or equivalent)
Develop scalable backend services and APIs using Python
Integrate AI agents with enterprise tools, APIs, and workflows
Evaluate, monitor, and optimize agent performance, reliability, and cost
Apply responsible AI principles including security, privacy, and governance
Stay current with advancements in LLMs, agent architectures, and Azure AI services
Required Qualifications
Bachelorโ€™s or Masterโ€™s degree in Computer Science, Engineering, or a related field
5+ years of experience in machine learning, AI engineering, or applied ML
Strong proficiency in Python for ML and backend development
Hands-on experience building LLM-based applications
Practical experience with agentic AI patterns (tool calling, planning, memory, reflection)
Experience with LangChain or similar agent orchestration frameworks
Solid understanding of RAG architectures
Experience with vector databases (e.g., Azure AI Search, Pinecone, etc.)
Familiarity with Azure cloud services and enterprise-grade deployments
Hands-on experience with MCP and/or A2A agent communication frameworks
Preferred Qualifications
Direct experience with Azure AI Foundry and Copilot Studio
Experience integrating AI agents into enterprise workflows or SaaS platforms
Knowledge of prompt engineering, evaluation frameworks, and guardrails
Experience with CI/CD, MLOps, or AI observability
Understanding of security, identity, and compliance in enterprise AI systems
Nice-to-Have
Contributions to AI prototypes, internal platforms, or open-source projects
Experience moving AI solutions from prototype to production
Strong communication skills and ability to explain complex AI systems to non-experts